@inproceedings{garcia-cumbreras-etal-2019-sinai,
title = "{SINAI}-{DL} at {S}em{E}val-2019 Task 7: Data Augmentation and Temporal Expressions",
author = "Garc{\'\i}a-Cumbreras, Miguel A. and
Jim{\'e}nez-Zafra, Salud Mar{\'\i}a and
Montejo-R{\'a}ez, Arturo and
D{\'\i}az-Galiano, Manuel Carlos and
Saquete, Estela",
editor = "May, Jonathan and
Shutova, Ekaterina and
Herbelot, Aurelie and
Zhu, Xiaodan and
Apidianaki, Marianna and
Mohammad, Saif M.",
booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S19-2196",
doi = "10.18653/v1/S19-2196",
pages = "1120--1124",
abstract = "This paper describes the participation of the SINAI-DL team at RumourEval (Task 7 in SemEval 2019, subtask A: SDQC). SDQC addresses the challenge of rumour stance classification as an indirect way of identifying potential rumours. Given a tweet with several replies, our system classifies each reply into either supporting, denying, questioning or commenting on the underlying rumours. We have applied data augmentation, temporal expressions labelling and transfer learning with a four-layer neural classifier. We achieve an accuracy of 0.715 with the official run over reply tweets.",
}
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%0 Conference Proceedings
%T SINAI-DL at SemEval-2019 Task 7: Data Augmentation and Temporal Expressions
%A García-Cumbreras, Miguel A.
%A Jiménez-Zafra, Salud María
%A Montejo-Ráez, Arturo
%A Díaz-Galiano, Manuel Carlos
%A Saquete, Estela
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%S Proceedings of the 13th International Workshop on Semantic Evaluation
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota, USA
%F garcia-cumbreras-etal-2019-sinai
%X This paper describes the participation of the SINAI-DL team at RumourEval (Task 7 in SemEval 2019, subtask A: SDQC). SDQC addresses the challenge of rumour stance classification as an indirect way of identifying potential rumours. Given a tweet with several replies, our system classifies each reply into either supporting, denying, questioning or commenting on the underlying rumours. We have applied data augmentation, temporal expressions labelling and transfer learning with a four-layer neural classifier. We achieve an accuracy of 0.715 with the official run over reply tweets.
%R 10.18653/v1/S19-2196
%U https://aclanthology.org/S19-2196
%U https://doi.org/10.18653/v1/S19-2196
%P 1120-1124
Markdown (Informal)
[SINAI-DL at SemEval-2019 Task 7: Data Augmentation and Temporal Expressions](https://aclanthology.org/S19-2196) (García-Cumbreras et al., SemEval 2019)
ACL